The current decade has seen an escalating interest in the blue economy, encompassing all economic activities that rely on the marine and coastal environment, with ocean-based economic activity projected to double by 2030 (OECD, 2016). Whilst the goods and services provided by the blue economy have conservatively been valued at over USD 2.5 trillion per annum (Hoegh-Guldberg et al. 2015), the IPCCs Special Report on the Oceans and Cryosphere in a Changing Climate, 2018 Living Planet Report (IPCC, 2019) and 2019 IPBES report (IPBES, 2019) provide strong evidence that the impacts of climate change and human activity are severely eroding ocean health and with it the resource base on which society and business depend.
The Blue Economy is of key importance to the global economy: value created and supported by our oceans, seas and coasts, is estimated to be worth at least USD 24 trillion. However, the direct and indirect value generated by marine environments is increasingly under threat from environmental drivers. This poses a risk to current and future assets and revenues dependent on a healthy Blue Economy. The relationships between environmental drivers and the Blue Economy are dynamic and nonlinear. Current approaches to evaluate the associated risks, such as Value at Risk (VaR) methodologies, are insufficient to account for such interactions, and the cumulative effects of drivers. Systems modeling is an approach which can capture complex dynamics between parameters. We explore the potential of using this approach to model and calculate financial risks in the Blue Economy. This paper describes the first iteration of exploring the approach, where we modeled two sectors located in the Baltic Sea: ports (shipping) and fisheries.
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